System, device and method for controlling an uncrewed aerial vehicle at public safety incidents

ABSTRACT

The present specification provides systems, devices and methods for controlling an uncrewed aerial vehicle (UAV) at a public safety incident. An example method contemplates placing a UAV in a shadow mode that follows a firefighter’s movements throughout the PSI while monitoring voice activity while the UAV performs tasks such as sending images from a camera to a central server. Potential voice commands are extracted from the voice activity and associated with tasks being performed by the UAV. A machine learning dataset is built from those associations such that at future incidents the UAV can operate in a freelance mode based on detected voice commands or other contextual factors.

BACKGROUND

Public safety incidents such as fires require rapid, professional andeffective responses to mitigate human injury and property damage.Technological enhancements can further improve outcomes of public safetyincidents, but many limitations remain.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying figures, where like reference numerals refer toidentical or functionally similar elements throughout the separateviews, together with the detailed description below, are incorporated inand form part of the specification, and serve to further illustrateembodiments of concepts that include the claimed invention, and explainvarious principles and advantages of those embodiments.

FIG. 1 is a block diagram representing a system for controlling at leastone uncrewed aerial vehicle (UAV) at public safety incidents.

FIG. 2 is a block diagram showing the server of FIG. 1 in greaterdetail.

FIG. 3 is a flow chart depicting a method of controlling a UAV in ashadow mode.

FIG. 4 is a flow chart depicting a method of monitoring a UAV in alearning mode.

FIG. 5 is a graphical representation of a UAV at a public safetyincident in the shadow mode of FIG. 3 and the learning mode of FIG. 4 .

FIG. 6 is a flow chart depicting a method of controlling a UAV in afreelance mode.

FIG. 7 is a graphical representation of a UAV at a public safetyincident in the freelance mode of FIG. 6 .

Skilled artisans will appreciate that elements in the figures areillustrated for simplicity and clarity and have not necessarily beendrawn to scale. For example, the dimensions of some of the elements inthe figures may be exaggerated relative to other elements to help toimprove understanding of embodiments of the present invention.

The apparatus and method components have been represented whereappropriate by conventional symbols in the drawings, showing only thosespecific details that are pertinent to understanding the embodiments ofthe present invention so as not to obscure the disclosure with detailsthat will be readily apparent to those of ordinary skill in the arthaving the benefit of the description herein.

DETAILED DESCRIPTION

Public safety incidents (PSI) encompass a broad category of incidentsincluding fires, vehicle accidents, search and rescue, shootings andacts of terrorism. Emergency services personnel (ESP) including firefighters, police and paramedics are the first responders to such PSIs,they are the front line workers who are tasked with professionally andefficiently bringing a particular PSI under control to mitigate humaninjury and loss of property. Technology affords many types of equipmentfor ESP to do their job well. Uncrewed Aerial Vehicles (UAVs) are anincreasingly common piece of equipment to support ESPs, often in theform of providing real time video surveillance of a PSI to help ESPunderstand the full context of a given situation and assist inprioritization, coordination and task allocation amongst ESP. However,the effort required to control the UAVs becomes another resource drainespecially when one of the ESP may be required to control the UAV. Forexample, in the context of firefighting, a firefighter is alreadywearing bulky equipment and must stay alert and focused on theever-present chaos of the fire. The benefits of UAV surveillance of thefire may be completely lost if the firefighter cannot otherwise focus onperforming firefighting tasks at hand. Indeed, controlling the UAV maybecome a dangerous distraction making the PSI even worse. UAV controlthus remains a serious barrier to deploying effective UAV surveillanceand other types of UAV tasks at a PSI. Thus, there exists a need for animproved technical method, device, and system for controlling uncrewedaerial vehicle (UAVs) at public safety incidents.

An aspect of the present specification provides a communication system,comprising a plurality of radio voice communication devices forperforming transmission and reception of first responder voicecommunications during a current public safety incident; an unmannedaerial vehicle (UAV) having an integrated communication device formonitoring the voice communications; and a processing unit forcontrolling the UAV and providing audio and voice analytics to detectand learn voice commands and associate the learned voice commands withUAV tasks assigned during the current public safety incident. In thesystem, the learned voice commands and associated UAV tasks for thecurrent public safety incident are processed for predictive analyticsfor automated control of the UAV at future public safety incidents usingvoice commands detected at the future incident.

The predictive analytics may include weighting the UAV tasks, combiningUAV tasks that can be performed together or prioritizing UAV tasks andupdating the weighting for use in future incident UAV responses.

The processing unit may further perform a radio frequency (RF) surveyvia the UAV communication device to identify personnel based on theirunique radio ID and associate public safety (PS) commands with UAV tasksassociated with the identified personnel and incident type.

The UAV may stream video of operations from a plurality of areas of theincident in conjunction with detected first responder voice commands.

The processing unit may add captions to UAV video that is notsynchronized with the first responder voice command to identifypotential anomalies, such as a time discrepancy between when the commandwas issued and when the task is performed.

The processing unit may detect a voice command at a future incident andaugments UAV tasks based on the detected command.

The processing unit may detect a location by understanding a physicallocation where a voice command originated and assessing a similar voicecommand from a nearby location.

The processing unit may further filter out commands that the UAV cannotperform and substitute a task that the UAV can perform related to thecommand.

The acquired data may be transferred to first responders based on firstresponder role and incident type.

Another aspect of the specification provides a method for controlling aUAV at a public safety incident, comprising: deploying a UAV at anincident scene, the UAV performing pre-defined tasks based on incidenttype: building a dataset of UAV tasks based on detected voice commandsand incident type; performing predictive analytics on the tasks forapplication at a future incident using the dataset; weighting andcombining tasks based on predetermined parameters; and automaticallyperforming predicted tasks at the future incident based on incidenttype. The method may further comprise using a machine learning system toperform the weighting and combining.

The above-mentioned aspects will be understood by the discussion belowin relation to certain non-limiting example embodiments. Such exampleembodiments are described with reference to certain systems, devices,methods and computer program products. It will be understood that eachblock of the flowchart illustrations and/or block diagrams, andcombinations of blocks in the flowchart illustrations and/or blockdiagrams, can be implemented by computer program instructions. Thesecomputer program instructions may be provided to a processor of ageneral purpose computer, a special purpose computer, or otherprogrammable data processing apparatus to produce a special purpose andunique machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks. The methods andprocesses set forth herein need not, in some embodiments, be performedin the exact sequence as shown and likewise various blocks may beperformed in parallel rather than in sequence. Accordingly, the elementsof methods and processes are referred to herein as “blocks” rather than“steps.”

These computer program instructions may also be stored in acomputer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer-readablememory produce an article of manufacture including instructions, whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer orother programmable data processing apparatus that may be on oroff-premises, or may be accessed via the cloud in any of a software as aservice (SaaS), platform as a service (PaaS), or infrastructure as aservice (IaaS) architecture so as to cause operational blocks to beperformed on the computer or other programmable apparatus to produce acomputer implemented process such that the instructions, which executeon the computer or other programmable apparatus, provide blocks forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. It is contemplated that any part of any aspector embodiment discussed in this specification can be implemented orcombined with other aspects or embodiments discussed in thisspecification.

Various advantages and features consistent with the presentspecification will become apparent from the following description withreference to the drawings.

Referring now to FIG. 1 , a system for controlling a uncrewed aerialvehicle (UAV) at a public safety incident (PSI) is indicated generallyat 100. The locus of the system 100 is a central server 104 that isconnectable via a communication link to at least one communicationdevice 108-1, 108-2 ... 108-n. (Collectively, the communication devices108-1, 108-2 ... 108-n will be referred to as communication devices 108,and generically, as communication device 108. This nomenclature is usedelsewhere herein.) Each device 108 may be assigned, even temporarily, toa respective firefighter 110. The central server 104 also connects to atleast one supervisory communication device 108-S, which is substantiallythe same as each communication device 108, except that device 108-S isused by supervisory personnel, such as a fire chief 118, and is used tocommunicate commands that coordinate activities amongst firefighters 110and to receive and display images of a PSI from the UAVs.

While the present embodiment refers to firefighters 110 and fire chief118, it is to be understood that firefighting is merely one type ofemergency service that can benefit from the present specification, andthat police officers and paramedics or other personnel may benefit.Regardless, it is to be understood that the technical benefits of thisspecification are agnostic to the personnel that are associated witheach device 108.

The central server 104 also maintains within local static memory atleast one dataset 112 and at least one application 114. Application 114is executable on a processor within server 104 and the at least onedataset 112 is accessible to at least one of the executing applications114. These aspects of central server 104 will be discussed in greaterdetail below.

Each communication device 108 comprises a headset 116 and a two-wayradio unit 120. Headset 116 is a form of input/output device connectedto unit 120 to allow for providing voice communication to a respectiveunit 120 and to produce audio signals that can be heard.

(From further reading of this specification, it will become understoodto a person of skill in the art that in variants of system 100, headset116 is not strictly necessary and that other means of communicatingbetween unit 120 and server 104 are contemplated. Additionally, theremay be only one headset 116, such as headset 116-S worn by fire chief118. By the same token, in other variants which will also becomeapparent from further reading of this specification, that communicationdevice 108 associated with a specific firefighter 110 may be obviatedaltogether when, for example, a given UAV 124 is configured to use imagerecognition to identify and follow a given firefighter 110.)

Each unit 120 is configured to communicate with server 104 and alsoconfigured to communicate via a wireless link to a respective uncrewedaerial vehicle 124 (also referred to as UAV 124). Units 120 or server104 can issue or relay control commands to UAVs 124, the details of suchcommands will be discussed in greater detail below. Each UAV 124 is alsoequipped with a camera for capturing still images or video images andrelaying those images back to unit 120 which in turn can relay suchimages to server 104. (Herein, the use of the term “images” on its ownis used interchangeably to refer to both still and video images and caninclude audio tracks captured with those images.) In other embodiments,UAVs 124 may also be equipped with other peripherals in addition, or inlieu of, a camera, such as temperature and gas sensors, a graspingmechanism for picking up, holding and releasing objects. A personskilled in the art will appreciate other possible peripherals and howthey can be controlled using this specification. Note that while system100 shows a plurality of UAVs 124, a variant of system 100 contemplateshaving only a single UAV 124.

Units 120 can each include a video display to view images captured byUAV 124. Notably, unit 120-S located within supervisory communicationdevice 108-S includes a display so that the fire chief 118 can monitorimage feeds from each UAV 124.

Referring now to FIG. 2 , a non-limiting example of the server 104 isshown in greater detail in the form of a block diagram. While the server104 is depicted in FIG. 2 as a single component, functionality of theserver 104 may be distributed amongst a plurality of components, such asa plurality of servers and/or cloud computing devices. Indeed, the term“server” itself is not intended to be construed in a limiting sense asto the type of computing hardware that may be used.

As depicted in FIG. 2 , the server 104 comprises: a communication unit202, a processing unit 204, and static memory 220. Varioussub-components of server 104 include a Random-Access Memory (RAM) 206,one or more wireless transceivers 208, one or more wired and/or wirelessinput/output (I/O) interfaces 210, a combined modulator/demodulator 212,a Read Only Memory (ROM) 214, a common data and address bus 216, acontroller 218, and a static memory 220 storing dataset(s) 112 andapplication(s) 114. The controller 218 is communicatively connected toother components of the server 104 via the common data and address bus216. Hereafter, the at least one application 114 will be interchangeablyreferred to as the application 114.

Furthermore, while the memories 206, 214 are depicted as having aparticular structure and/or configuration, (e.g., separate RAM 206 andROM 214), memory of the server 104 may have any suitable structureand/or configuration.

While not depicted, the server 104 may include one or more of an inputdevice and/or output device such as a display screen, which, whenpresent, is communicatively coupled to the controller 218.

As shown in FIG. 2 , the server 104 includes the communication unit 202communicatively coupled to the common data and address bus 216 of theprocessing unit 204.

The processing unit 204 may include the Read Only Memory (ROM) 214coupled to the common data and address bus 216 for storing data forinitializing system components. The processing unit 204 further includesthe controller 218 being coupled by the common data and address bus 216to the Random-Access Memory 206 and the static memory 220.

The communication unit 202 may include one or more wired and/or wirelessinput/output (I/O) interfaces 210 that are configurable to communicatewith other components of the system 100. For example, the communicationunit 202 may include one or more wired and/or wireless transceivers 208for communicating with other components of the system 100. Hence, theone or more transceivers 208 may be adapted for communication with oneor more communication links and/or communication networks used tocommunicate with the other components of the system 100. For example,the one or more transceivers 208 may be adapted for communication withone or more of the Internet, a digital mobile radio (DMR) network, aProject 25 (P25) network, a terrestrial trunked radio (TETRA) network, aBluetooth network, a Wi-Fi network, for example operating in accordancewith an IEEE 802.11 standard (e.g., 802.11a, 802.11b, 802.11g), an LTE(Long-Term Evolution) network and/or other types of GSM (Global Systemfor Mobile communications) and/or 3GPP (3rd Generation PartnershipProject) networks, a 5G network (e.g., a network architecture compliantwith, for example, the 3GPP TS 23 specification series and/or a newradio (NR) air interface compliant with the 3GPP TS 38 specificationseries) standard), a Worldwide Interoperability for Microwave Access(WiMAX) network, for example operating in accordance with an IEEE 802.16standard, and/or another similar type of wireless network. Hence, theone or more transceivers 208 may include, but are not limited to, a cellphone transceiver, a DMR transceiver, P25 transceiver, a TETRAtransceiver, a 3GPP transceiver, an LTE transceiver, a GSM transceiver,a 5G transceiver, a Bluetooth transceiver, a Wi-Fi transceiver, a WiMAXtransceiver, and/or another similar type of wireless transceiverconfigurable to communicate via a wireless radio network.

The communication unit 202 may further include one or more wirelinetransceivers 208, such as an Ethernet transceiver, a USB (UniversalSerial Bus) transceiver, or similar transceiver configurable tocommunicate via a twisted pair wire, a coaxial cable, a fiber-opticlink, or a similar physical connection to a wireline network. Thetransceiver 208 may also be coupled to a combined modulator/demodulator212.

A person skilled in the art will now recognize that the communicationunit 202 provides the point of connection between the server 104 and thecommunication devices 108 of FIG. 1 . It will also now be understoodthat, in accordance with FIG. 1 , a wireless communication link betweenthe server 104 and the devices 108 is contemplated. With that said, thechoice of wired or wireless communication links anywhere in system 100is not specifically mandated unless required to fulfill a givenfunction.

The processing unit 204 may include ports (e.g., hardware ports) forcoupling to other suitable hardware components of the system 100. Thecontroller 218 may include one or more logic circuits, one or moreprocessors, one or more microprocessors, one or more GPUs (GraphicsProcessing Units), and/or the controller 218 may include one or moreASIC (application-specific integrated circuits) and one or more FPGA(field-programmable gate arrays), and/or another electronic device. Insome examples, the processing unit 204 and/or the server 104 is not ageneric controller and/or a generic device, but a device specificallyconfigured to implement functionality for controlling one or more UAVs.For example, in some examples, the server 104 and/or the processing unit204 specifically comprises a computational engine configured toimplement functionality for controlling UAVs.

The static memory 220 comprises a non-transitory machine readable mediumthat stores machine readable instructions to implement one or moreprograms or applications. Example machine readable media include anon-volatile storage unit (e.g., Erasable Electronic Programmable ReadOnly Memory (“EEPROM”), Flash Memory). In the example of FIG. 2 ,programming instructions (e.g., machine readable instructions) thatimplement the functionality of the server 104 as described herein aremaintained, persistently, at the memory 220 and used by the processingunit 204, which makes appropriate utilization of volatile storage duringthe execution of such programming instructions.

Furthermore, the memory 220 stores instructions corresponding to the atleast one application 114 that, when executed by the controller 218,enables the controller 218 to implement functionality for controllingUAVs, including but not limited to, the blocks of certain methodsdiscussed elsewhere herein. Furthermore, while the datasets 112 areshown as stored within memory 220, they can also be maintained indatabase servers (not shown) external to server 104. Likewise,applications 114 may be stored externally to server 104.

In illustrated examples, when the controller 218 executes the one ormore applications 114, the controller 218 is configured to performvarious methods including the specific methods and their variantsdiscussed below.

Alternatively, or in addition, each application 114 may include machinelearning and/or deep-learning based algorithms and/or neural networks,and the like, which can be trained to improve the UAV control approachesdiscussed herein. Furthermore, in these examples, each application 114may be operated by the controller 218 in a training mode to train themachine learning and/or deep-learning based algorithms and/or neuralnetworks of each application 114 in accordance with the teachingsherein.

The one or more machine-learning algorithms and/or deep learningalgorithms and/or neural networks of each application 114 may include,but are not limited to: a generalized linear regression algorithm; arandom forest algorithm; a support vector machine algorithm; a gradientboosting regression algorithm; a decision tree algorithm; a generalizedadditive model; neural network algorithms; deep learning algorithms;evolutionary programming algorithms; Bayesian inference algorithms;reinforcement learning algorithms, and the like. However, generalizedlinear regression algorithms, random forest algorithms, support vectormachine algorithms, gradient boosting regression algorithms, decisiontree algorithms, generalized additive models, and the like may bepreferred over neural network algorithms, deep learning algorithms,evolutionary programming algorithms, and the like. However, generalizedlinear regression algorithms, random forest algorithms, support vectormachine algorithms, gradient boosting regression algorithms, decisiontree algorithms, generalized additive models, and the like may bepreferred over neural network algorithms, deep learning algorithms,evolutionary programming algorithms, and the like, in some public-safetyenvironments. However, to be clear, any suitable machine-learningalgorithm and/or deep learning algorithm and/or neural network is withinscope of the present specification.

Referring again to FIG. 1 , while details of the devices 108 are notdepicted, the devices 108, and particularly the units 120, may havecomponents similar to the server 104 but adapted for their respectivefunctionalities and form factor. For example, the units 120 may includerespective display screens for rendering notifications and displayingimages and/or respective devices and/or applications for registering alocation with the server 104, and the like. As noted, the headsets 116are input devices including a microphone for receiving voiceinstructions, and various output devices, such as a speaker or headsetinterface, for delivering voice responses. The devices 108 thus have aform factor and interfaces that make them suitable for use byfirefighters, and accordingly, one device 108 is shown respective toeach firefighter 110.

FIG. 1 also shows a plurality of UAVs 124. Each UAV 124 itself has acomputational unit with a set of components similar to server 104, butalso with its own form factor and structure that complements the formfactor of the UAV 124. In other words, each computational unit withineach UAV 124 has its own processing unit, memory, communication unit,peripherals and independent power supply that are configured for themechanical and necessary control functions of the UAV 124.

It is to be understood that specific computational and storagefunctions, including datasets 112 and applications 114, described inrelation to each of server 104, devices 108 and UAVs 124 may beimplemented across different components of system 100, and the specificembodiments described herein are examples. As a particular example, UAVs124 are described herein with limited computational resources and to actas slaves to control commands determined and issued by server 104 viaunits 120; and yet, in variants of system 100, UAVs 124 can includeadditional computational resources to permit performance of some or allof the applications 114 and/or datasets 112 to be locally resident atUAV 124.

Speaking generally, a person of skill in the art, with the benefit ofthis specification, will appreciate that system 100 is intended tooperate in a supporting role for firefighters 110 performing theirduties at a fire or other public safety incident. More specifically,UAVs 124 are configured to provide image telemetry via system 100, so asto allow the server 104 and the devices 108 to use the remainingcomputing resources of system 100 for other functions thus overallimproving optimization of the computing resources of system 100.

To further assist in understanding system 100, reference will now bemade to FIG. 3 , which shows a flowchart indicated generally at 300depicting a method for controlling a UAV in a shadow mode. Hereafter,the flowchart will be referred to as method 300, and this nomenclaturewill apply to other methods and flowcharts discussed herein. Method 300can be implemented on system 100, but it is to be understood that method300 can also be implemented on variations of system 100, and likewise,method 300 itself can be modified and operate on system 100. It is to beunderstood that the blocks in method 300 need not be performed in theexact sequence shown and that some blocks may execute in parallel withother blocks, and method 300 itself may execute in parallel with othermethods. Additional methods discussed herein in relation to system 100are subject to the same non-limiting interpretation.

For illustrative convenience, method 300 will now be discussed inrelation to system 100, and the integration of method 300 into system100 is represented by the inclusion of application 114-1 in FIG. 3 ,indicating that method 300 is implemented as application 114-1 in FIG. 1. Thus, method 300 and application 114-1 may be referred tointerchangeably, and this convention will also apply elsewhere in thisspecification.

In a general sense, method 300 and application 114-1 represent a shadowmode, whereby a given UAV is configured to shadow the movements of agiven firefighter. In system 100, when operating in shadow modeaccording to method 300, each UAV 124 will track the movements of arespective firefighter 110 and capture and relay image data to server104. There are many ways to implement application 114-1 itself, and ofparticular note, application 114-1 may be implemented, substantially orin part, within the computational resources inside each UAV 124, or,substantially or in part, inside the computational resources of arespective device 108. In the present example, however, application114-1 is implemented inside server 104 and thus multiple instances ofapplication 114-1 can execute inside server 104 to control differentrespective UAVs 124. According to this example, the computationalresources inside UAV 124 are minimal while the core computationalfunctions of method 300/application 114-1 are substantially offloaded tothe server 104, establishing a slave relationship to the server 104.

Referring now to FIG. 3 , at block 304, a pairing function is effected.Such a pairing function establishes a communication association betweena UAV 124 and its respective communication device 108 and/or firefighter110. The means by which such pairing is effected is not particularlylimited, but as an illustrative example, can include initializing theUAV 124 and providing an express communication identifier (e.g. a MACaddress, IP address) of a device 108 and thereby establishing acommunication link that pairs the UAV 124 with the communication device108. Alternatively, or in addition, a firefighter 110 may wear a visualidentifier on her personal protective equipment (“PPE”) or “bunker gear”that the camera within a UAV 124 is configured to image-capture and theprocessing unit within UAV 124 configured to recognize. Other means ofeffecting a pairing between a given UAV 124 and a given communicationdevice 108 and/or firefighter 110 will now occur to those skilled in theart. (It is to be understood that in variants of system 100, the conceptof pairing at block 304 need not be construed in a limiting fashion, asserver 104 can be configured to dynamically change which UAV 124 isassociated with a given firefighter 110, or indeed, in a subsequentembodiment discussing “freelancing mode” where a given UAV 124 need nothave any specific pairing with a given firefighter 110 at all but simplyperforms tasks assigned by server 104.)

At block 308, tracking connections are established. Such trackingconnections are to establish the sensory communications between themovements of the firefighter 110 and its respective UAV 124, such thatthe UAV 124 can be controlled to follow the movements of the firefighter110 using location data in device 108 and/or artificial intelligenceimage processing to visually identify firefighter 110 and issue controlsto cause the UAV 124 to follow the movements of the firefighter 110. Ifdesired, as an enhancement, enhanced tracking connections can beemployed to also follow the head and/or eye movements of the firefighter110 so that the camera in the UAV 124 is capturing images of objectsthat are also being seen by the firefighter 110. In a simple enhancedconfiguration, the UAV 124 may use artificial intelligence imageprocessing so as to identify the front and back of the firefighter 110,so that it can orient its camera in the same direction as the front ofthe firefighter 110. In more complex configurations, headset 116 can beaugmented with tracking sensors such as those employed in certaincommercially available virtual reality headsets. By the same token, UAV124 can be equipped with one or more omni-directional cameras andthereby obviate or mitigate the need for direction tracking. The personof skill in the art will now appreciate other means of establishingtracking connections.

At block 312, flight of the UAV is initiated. Thus at block 312, thepropulsion system of the UAV 124 is activated and according to block316, propulsion and directional control commands are issued to the UAV124, instructing the UAV 124 to follow the movements of firefighter 110according to the pairing and tracking connections established in block304 and block 308 respectively. At block 320, images are captured by thecamera system of the UAV 124 according to the tracking technology as perblock 308. At block 324, images captured at block 320 are transmitted toserver 104 for storage, analytics, relaying and/or display on, forexample, the display within one more units 120, such as unit 120-S sothat fire chief 118 can monitor the visual experience of the firefighter110 respective to the UAV 124. In this manner, fire chief 118 canunderstand the experiences of the firefighters 110 and issue commandinstructions to the firefighters with the view towards resolving thePSI.

At block 330, a decision is made as to whether to interrupt theperformance of method 300. A “no” decision, meaning that method 300 isto continue, results in a return to performance of block 316, block 320and block 324 so that the UAV 124 continues to follow the firefighter110 and relay images to server 104 as described above. (Collectively,block 320 and block 324 may be considered the performance of one type of“task” that can be performed by a UAV 124 and such a “task” mayoptionally be considered to include a given location or movement patternfrom block 316.)

A “yes” decision at block 330 leads to block 334, which broadlyencompasses the option to execute another function.

The other function at block 334 could simply be a power-down command tothe UAV 124, thereby terminating operation of UAV 124 altogether,preceded by a graceful return and landing of the UAV 124. Block 334contemplates that one or more other functions could be invoked, andwhich function(s) is(are) invoked can be managed by having more complexdecision criteria at block 330.

The other function at block 334 can also be another operating mode ofUAV 124 that either executes in lieu of method 300 or in parallel withmethod 300. Recall that method 300 is a shadow mode whereby UAV 124follows firefighter 110, but other modes can include a variety of otherfunctions such as picking up objects, taking gas or temperature readingsif so equipped, or assumption of pure manual control over the UAV 124 bya device 108 or the like.

In the present specification, one contemplated function that can runparallel to method 300 is a learning mode, which is discussed in greaterdetail in FIG. 4 in relation to method 400. In a general sense, thelearning mode contemplates monitoring voice activity that is capturedsimultaneously, or temporally relative to, the specific “task” beingperformed at, for example, block 316, block 320 and block 324.

Referring now to FIG. 4 , block 404 comprises monitoring the currenttask. As noted above, a “task” can comprise any activity that is beingperformed by the UAV at the time of performance of block 404. As notedabove, a “task” can be deemed to be the capturing and transmission ofimages by the camera of the UAV 124, i.e. what is occurring at block 320and block 324 in association with a given location or movement patternfor the UAV 124. In variants, tasks can refer to any function beingperformed by the UAV 124, such as moving from one location to another,performing a grasping or releasing of an object, performing a gas ortemperature sensor reading or any other task that can be performed by aUAV according to the UAV’s features and functions. “Task” should beconstrued broadly.

At block 408, current voice activity is monitored. Block 408contemplates that voice activity received at a given headset 116 ismonitored. In a simple case, the headset 116 respective to a given UAV124 is monitored in order to eventually extract voice commands from thevoice activity that coincide with a current UAV task. In more complexcases all headsets 116 are monitored, and specifically the headset 116-Sbelonging to fire chief 118, to develop more complex models of extractedvoice commands associated with UAV tasks. The simple case will bediscussed in greater detail below and a person of skill in the art willtherefore come to appreciate the more complex cases and scaling. Thevoice activity from block 408 is essentially raw data that stored in avoice-activity dataset 112-1 for further analysis.

At block 412, voice commands suitable for controlling UAV 124 areextracted from the voice activity monitored at block 408. Block 412 canbe effected by known natural language processing (NLP) techniques orhuman machine learning training or a combination of both. Phrases suchas “Show me the other side of the building”, “Show me the oppositecorner”, “What’s behind the wall?” or “Show me the view from the end ofthe street” would be extracted from the voice-activity dataset 112-1 andstored in a voice-command dataset 112-2. On the other hand, voiceactivity such as “My air tank is full” or “Start connecting hoses to thesecond pumper truck” would be ignored as being irrelevant forcontrolling the UAV.

At block 416, a UAV task is associated with an appropriate extractedvoice command. Block 412 can be effected by a simple temporalassociation between the time when a given UAV task was being performedand an extracted voice command from block 412. Thus, for example, if thevoice command “Show me the rear of the building” was extracted fromvoice communications originating from headset 116-S operated by firechief 118, and within a subsequent time interval firefighter 110-1 walksto the rear of a building while method 300 is controlling UAV 124-1, andfirefighter 110-1 issues a response message “I am at the rear of thebuilding and looking at it”, then the specific UAV task encompassed bythe movement performed at block 316, the image capture performed atblock 320 and the image transmission performed at block 324, can beassociated with the voice command “Show me the rear of the building”. Atthis point, this combination of the extracted voice command and the taskcan be associated and stored in a command-task dataset 112-3 for futureuse.

(As will be elaborated below in relation to freelancing mode in FIG. 6 ,at this point a person of skill in the art will begin to recognize that,as a result of the teachings in this specification, each UAV 124 can bedynamically assigned one, or more, learned tasks stored in dataset112-3, and each task can be associated with different types of publicsafety incidents. Thus, in the freelancing mode to be discussed below, agiven UAV 124 can also be configured to execute a given “action” taskautomatically when it associates the current situation with a related“monitor” task. For example, when a UAV 124 is controlled so as to“Monitor the rear of the building” during a fire public safety incident,the UAV 124 will periodically send video of the rear of the building. Ina police crime scene, when the UAV 124 is assigned a task correspondingto “Monitor the back of the parking garage”, then the UAV 124 can becontrolled so as to send an audio alert and video whenever a car exitsthe garage. In a search and rescue incident, the command “Monitor thetrail entrance” can result in the UAV 124 being configured to deliver anaudio alert, a video feed, and/or a thermal image whenever a person isdetected at the trail entrance. Again, these aspects of thespecification will become more apparent from the following discussionsbelow.)

Referring again to FIG. 4 , at block 420 a confidence interval isassigned to the command and task association from block 416. This can bepart of a machine learning training exercise that is done manually by ahuman reviewing the image feeds and providing an affirmation that thecommand and task were correctly associated. Alternatively, or inaddition, the machine learning can also be automated based on a voiceresponse message such as “I am at the rear of the building and lookingat it” can be considered part of a machine learning training set thatassigns an affirmative confirmation that the command was correctlyassociated with the task.

Referring again to FIG. 4 , block 424 contemplates an interruptdecision, where a “no” determination results in continued training ofassociating voice commands with UAV tasks as described above, or a “yes”determination leads to block 428 at which point another function can beexecuted. The criteria for block 424 is not particularly limited and isleft to a person skill in the art having the benefit of thisspecification to consider a full range possibilities according to thecontext in which system 100 may be operated, but a concrete examplewould be the issuance of an express command that the tasks beingperformed by UAV 124 are complete and further training under method 400is not presently required. The other functions that can be performed atblock 428 are not particularly limited but could include terminatingmethod 400 and invoking method 500 in FIG. 6 , or continuing to performmethod 300, method 400 and/or alternating method 300 with performance ofmethod 500 in FIG. 6 .

To provide further illustration of the parallel performance of method300 and method 400, FIG. 5 shows a graphic. The graphic provides avisual of the firefighter 110-1 and UAV 124-1 operating in shadow modeaccording to method 300 and whereby the functions being performed by UAV124-1 are being captured as a discrete task according to method 400 andassociated with extracted voice commands, in order to build thecommand-task dataset 112-3.

Referring now to FIG. 6 , a method 500 (which corresponds to application114-3) is shown for controlling a UAV in a freelancing mode, whereby agiven UAV 124 is controlled based on extracted voice commands from itsrespective headset 116 or another headset within system 100. Freelancingmode becomes available for controlling a UAV after a machine learningtraining exercise has been conducted using method 400 or the like. In ageneral sense, when operating in freelancing mode according to method500, a UAV 124 will be controlled when an extracted voice commandmatches a known task stored in the command-task dataset 112-3. Again,there are many ways to implement application 114-3 itself, and ofparticular note, application 114-3 may be implemented, substantially orin part, within the computational resources inside each UAV 124, or,substantially or in part, inside the computational resources of arespective device 108. In the present example, however, application114-3 is implemented inside server 104 and thus multiple instances ofapplication 114-3 can execute inside server 104 to control differentrespective UAVs 124. According to this example, the computationalresources inside UAV 124 are minimal while the core computationalfunctions of method 500/application 114-3 are substantially offloaded tothe server 104, establishing a slave relationship with the server 104.

It should be noted that, for illustrative simplicity, method 500 omitsreproducing the pairing, tracking and flight initiation blocks frommethod 300 (i.e. block 304, block 308 and block 312). However a personof skill in the art will appreciate that those blocks are assumed tohave been performed before block 504. Also, the same comments maderegarding the other methods regarding the possibility of parallel blockperformance and/or different order of block performance apply to method500.

At block 504 voice activity is monitored. Block 504 is substantiallyequivalent to block 408 of method 400, except modified as desired forthe context of method 500. Likewise, block 508 comprises extraction ofvoice commands from the voice activity monitored at block 504 andtherefore block 508 is substantially equivalent to block 412 of method400. At block 512, a determination is made as to whether any voicecommands extracted at block 508 match a known voice command that isassociated with a known task. In the non-limiting example of system 100,block 512 is effected by examining command-task dataset 112-3.

Based on the specific example from the discussion of method 400, aperson of skill in the art will now appreciate that, at block 512, if anextracted voice command matches “Show me the rear of the building”, thena corresponding task will be available in dataset 112-3, as recordedduring a previous PSI, based on the UAV movements and camera capturingshown and previously discussed in relation to FIG. 5 . Thus, in thisexample, a “yes” determination will be made at block 512, leading toblock 516.

At block 516, the UAV task found in command-task dataset 112-3 thatmatched the extracted voice command from block 508 is automaticallyperformed by server 104 (or other component of system 100) issuingcontrol commands to UAV 124-1 to cause UAV 124-1 to perform the sametask that was shown and discussed in relation to FIG. 5 . Specifically,as illustrated in FIG. 7 , UAV 124-1 is controlled without shadowingfirefighter 110-1 and instead is controlled to move towards the rear ofthe building and to provide images of the rear of the building to server104 for use in whatever fashion is desired for managing the PSI.

It is to be emphasized that the specific example in FIG. 7 is both acontemplated example for actual implementation and yet is also asimplified example. At the end of the discussion of method 500, manycomplex enhancements to method 500 are discussed. However, to brieflyillustrate an example of a complex enhancement, it is contemplated thatblock 516 can include the combining and/or prioritization of performanceof several tasks in association with a plurality of voice commands thatare extracted at block 508. For example, if, at block 508, two commandsare extracted, such as “Enter shadow mode for firefighter 110-2” and“Monitor of the rear of the building”, then at block 516, UAV 124 can becontrolled so as to choose a route between the rear of the building andthe location of firefighter 110-2 so that during flight towardsfirefighter 110-2, the UAV 124 will pass by the rear of the building tocapture one last sweep. Alternatively, if UAV 124 has a 360 degreecamera, or multiple cameras, the UAV 124 can attempt to monitor both thebuilding and shadow firefighter 110-2 as long as the location offirefighter 110-2 allows the cameras of UAV 124 to also continuemonitoring the rear of the building. Where a combination of the tasks isnot possible, then the UAV 124 can be controlled to prioritize, forexample, the shadow command over the monitor command. In this context, askilled person will understand that block 512 is thereby enhanced toconsider whether “Voice commands match more than one known task?”. Atthis point a further determination is made as to whether those knowntasks can be combined, or if a priority should be assigned as to whichknown task should be performed first, or if the tasks can be performedin parallel.

Returning to our simple example, however, if, at block 512 the extractedvoice commands at block 508 do not match any known tasks, then a “No”determination is made at block 512 and block 520 is invoked. Block 520comprises executing another function, and again, the type of function isnot particularly limited, but may include reverting control of the UAV124 to the shadow mode as previously discussed in relation to method300. However, other functions can be invoked here according to thedesired implementation of system 100, such as shutting down the UAV 124,or, maintaining UAV 124 in a hovering state on standby and simplyadvancing to block 524.

At block 524, an interrupt determination is made, similar to theinterrupt determination from block 330 or the interrupt determinationfrom block 424. A “yes” determination leads to block 520, while a “no”determination returns method 500 to block 504.

Persons skilled in the art will also now appreciate that frequent anddynamic updating of datasets 112 bolsters the accuracy of command-taskassociations within system 100, thereby increasing confidence in thesuccess of effecting the desired control of UAV 124 in freelancing mode.Thus, constant monitoring of voice commands over headsets 116 and tasksperformed by UAV 124, coupled with applications that constantly assessassociations between those commands and tasks, can lead to increasedconfidence intervals in the accuracy of the datasets 112 andaccompanying freelancing UAV activity of method 500. Additionally,firefighters 110 or other individuals may periodically manually affirmor deny the accuracy of the determinations made at block 512 by studyingcaptured voice commands and comparing them with UAV tasks that have beenperformed in shadow mode under method 300. These affirmations or denialscan be fed back into datasets 112, particularly the confidence intervaldata (which may also be referred to as weightings or relationshipweightings) such that the command-task associations are sufficient or“good enough”, thereby increasing the quality of datasets 112 and futuredeterminations made at block 512. Accordingly, persons skilled in theart will now also appreciate the novel machine learning aspect of thepresent specification. Indeed, one or more machine-learning algorithmsfor implementing a machine-learning feedback loop for training the oneor more machine-learning algorithms may be provided, where themachine-learning feedback loop comprises processing feedback indicativeof an evaluation of confidence intervals maintained within datasets 112to as determined by the one or more machine-learning algorithms.

Indeed, confidence intervals may be provided as a training set offeedback for training the one or more machine-learning algorithms tobetter determine these command-task relationships. Such a training setmay further include factors that lead to such determinations, including,but not limited to, manually affirming or denying “yes” determinationsmade at block 512. Such a training set may be used to initially trainthe one or more machine learning algorithms.

Further, one or more later determined confidence intervals may belabelled to indicate whether the later determined confidence intervals,as generated by the one or more machine-learning algorithms, representpositive (e.g., effective or “good enough”) examples or negative (e.g.,ineffective) examples.

For example, the one or more machine-learning algorithms may generateconfidence intervals, to indicate higher or lower levels of respectiveconfidence in correctly associating a given voice command with a giventask.

Regardless, when confidence intervals in datasets 112 are provided toone or more machine-learning algorithms in the machine-learning feedbackloop, the one or more machine-learning algorithms may be better trainedto determine future confidence intervals.

In other examples, confidence intervals generated by one or moremachine-learning algorithms may be provided to a feedback computingdevice (not depicted), which may be a component of the system 100 and/orexternal to the system 100 that has been specifically trained to assessaccuracy of “yes” determinations made at block 512. Such a feedbackcomputing device may generate its own confidence intervals as feedback(and/or at least a portion of the feedback) back to the server 104 forstorage (e.g., at the memory 220) until a machine-learning feedback loopis implemented. Put another way, confidence intervals for command-taskassociations via a machine learning algorithm may be generated and/orprovided in any suitable manner and/or by any suitable computing deviceand/or communication device.

Hence, by implementing a machine-learning feedback loop, more efficientoperation of server 104 may be achieved, and/or a change in operation ofthe server 104 may be achieved, as one or more machine-learningalgorithms are trained to better and/or more efficiently determine theconfidence intervals in datasets 112.

Persons of skill in the art will now appreciate that system 100 andrelated datasets 112 and applications 114 can be scaled and thatcombinations, subsets and variations are contemplated. The presentspecification generally provides a system which is used to communicatebetween human users at a public safety incident that contemplatesmonitoring the voice communications between the human users on thesystem and which uses audio and voice analytics to decode commands forhuman users. The UAV can learn the intent of these commands fromprevious incidents and apply that intent during a current incident.

Several additional possible enhancements to system 100 are contemplated:

-   1) The UAV 124 is configured to stream video of operations from    various areas of the incident as those areas are mentioned over the    voice channels of system 100.-   2) If multiple areas of the PSI are mentioned, the UAV 124 may    alternate between them or include all views in a wider angle.-   3) Audio captions in the form of text may be added to the captured    video which is analyzed and annotated to indicate what audio is not    synchronized with a task and to indicate why and when the actual    task is being performed. This may happen when UAV task performance    is delayed due to UAV travel time or due to another task having a    higher priority, all of which can be part of the machine learning    training discussed above.-   4) The UAV controls may be learned from previous PSI such as fires    such that that the first fire unit arriving can visualize the    structure from all sides as a secondary size-up to better understand    the PSI, so when system 100 is fully trained, the UAV performs this    task without human prompting upon arrival at a structure fire or    other PSI.-   5) The system 100 will interpret “monitor” commands as: provide    video and sensor data of an area periodically. Such as: “monitor the    rear of the structure”-   6) The system 100 is trained to discern which commands the UAV can    perform or augment and automatically provides supporting video or    other sensor data to support human users, actions and commentary.    For example, a UAV that cannot enter a structure without its own    destruction or damage will ignore commands intended for interior    crews but the UAV can still stream video of an exterior in the    vicinity of interior crew. In this way, system 100 is configured to    filter out certain commands from other commands.-   7) The system 100 will determine which data to send for action based    on: user role and incident size. For example: video can go to the    fire chief or other incident commander while a short verbal    description of the video can be sent as update to all users.-   8) The system 100 can be configured so that captured images only    intended for a specific subset of firefighters 110 or fire chief 118    are delivered to them. For example, images captured by UAV 124-1 may    be only delivered by server 104 to unit 120-S and unit 120-1, but    not delivered to the remaining units 120.

Further enhancements include a method for combining tasks associatedwith voice commands and UAVs 124 at a given PSI. For example, system 100can be configured to have different incident types associated withdifferent tasks maintained in datasets 112. On this basis, system 100can be configured so that a given UAV 124 can begin performingpre-defined tasks that are based on incident type. Incident types caninclude a fire, a burglary, a vehicle collision, a shooting, an act ofterrorism or a search and rescue mission. Other incident types willoccur to those skilled in the art.

Method 300 can also be scaled or modified so that different oradditional datasets 112 are assigned to different firefighters 110.Thus, the shadow function of method 300 and learning function of method400 as performed in relation to firefighter 110-1 may create acommand-task dataset 112-3 that is different from the command-taskdatasets 112-3 that would be created in relation to each otherfirefighter 110-2 ... 110-n. Associations between a given firefighter110, the tasks they perform and their voice commands can be based on anaerial survey to identify personnel using image recognition or based ontheir unique radio ID associated with their unit 120. Firefighters 110and other personnel can be identified via a dataset 112 including viaattributes such as location, rank, time on scene or order of arrival.

The freelance mode of method 500 can be enhanced such that as multiplevoice commands are extracted, performance of tasks associated with thosecommands are prioritized based on, for example:

-   1) Urgency of request based on machine learning that detects stress    levels in voice-   2) Urgency of request determined by keywords-   3) Ability to combine a given task with other tasks-   4) An affirmative command indicating priority request-   5) Role/rank of firefighter 110 or fire chief 118 or other personnel-   6) Time sensitive requests, such as obtaining a reading of gas    levels that may predict an imminent explosion-   7) Current location and capabilities of UAVs-   8) Prioritizing tasks for the shortest path between the UAV 124 and    the task location-   9) Prioritization levels based on learned tasks from previous    incidents

The freelance mode of method 500 can be enhanced by training such thatas multiple voice commands are extracted, performance of tasksassociated with those commands may, in addition or instead of beingprioritized, the system 100 can be configured to determine whichrequests can be combined based by taking into account: 1) weightingfactors as defined in the datasets 112 relating to the source of thevoice command; 2) content of the voice command; 3) context of voicecommand such as location; 4) tasks with similar priority level and 5)establishing an overall priority of a combined set of tasks.

The freelance mode of method 500 can be enhanced by location awareness,by understanding the physical location where a voice command originated,and the analysis of one or more similar voice commands from a nearbylocation.

In other variations, other inputs from the firefighters can be usedother than voice commands, such as body gestures or direct input intounits 120.

As will now be apparent from this detailed description, the operationsand functions of electronic computing devices described herein aresufficiently complex as to require their implementation on a computersystem, and cannot be performed, as a practical matter, in the humanmind. Electronic computing devices such as set forth herein areunderstood as requiring and providing speed and accuracy and complexitymanagement that are not obtainable by human mental steps, in addition tothe inherently digital nature of such operations (e.g., a human mindcannot interface directly with RAM or other digital storage, cannottransmit or receive electronic messages, cannot control a displayscreen, cannot implement a machine learning algorithm, nor implement amachine learning algorithm feedback loop, and the like).

In the foregoing specification, specific embodiments have beendescribed. However, one of ordinary skill in the art will now appreciatethat various modifications and changes can be made without departingfrom the scope of the invention as set forth in the claims below.Accordingly, the specification and figures are to be regarded in anillustrative rather than a restrictive sense, and all such modificationsare intended to be included within the scope of present teachings. Thebenefits, advantages, solutions to problems, and any element(s) that maycause any benefit, advantage, or solution to occur or become morepronounced are not to be construed as a critical, required, or essentialfeatures or elements of any or all the claims. The invention is definedsolely by the appended claims including any amendments made during thependency of this application and all equivalents of those claims asissued.

Moreover in this document, relational terms such as first and second,top and bottom, and the like may be used solely to distinguish oneentity or action from another entity or action without necessarilyrequiring or implying any actual such relationship or order between suchentities or actions. The terms “comprises,” “comprising,” “has”,“having,” “includes”, “including,” “contains”, “containing” or any othervariation thereof, are intended to cover a non-exclusive inclusion, suchthat a process, method, article, or apparatus that comprises, has,includes, contains a list of elements does not include only thoseelements but may include other elements not expressly listed or inherentto such process, method, article, or apparatus. An element proceeded by“comprises ... a”, “has ... a”, “includes ... a”, “contains ... a” doesnot, without more constraints, preclude the existence of additionalidentical elements in the process, method, article, or apparatus thatcomprises, has, includes, contains the element. The terms “a” and “an”are defined as one or more unless explicitly stated otherwise herein.The terms “substantially”, “essentially”, “approximately”, “about” orany other version thereof, are defined as being close to as understoodby one of ordinary skill in the art. Furthermore, references to specificpercentages should be construed as being “about” the specifiedpercentage.

A device or structure that is “configured” in a certain way isconfigured in at least that way, but may also be configured in ways thatare not listed.

It will be appreciated that some embodiments may be comprised of one ormore generic or specialized processors (or “processing devices”) such asmicroprocessors, digital signal processors, customized processors andfield programmable gate arrays (FPGAs) and unique stored programinstructions (including both software and firmware) that control the oneor more processors to implement, in conjunction with certainnon-processor circuits, some, most, or all of the functions of themethod and/or apparatus described herein. Alternatively, some or allfunctions could be implemented by a state machine that has no storedprogram instructions, or in one or more application specific integratedcircuits (ASICs), in which each function or some combinations of certainof the functions are implemented as custom logic. Of course, acombination of the two approaches could be used.

Moreover, embodiments can be implemented as a computer-readable storagemedium having computer readable code stored thereon for programming acomputer (e.g., comprising a processor) to perform a method as describedand claimed herein. Any suitable computer-usable or computer readablemedium may be utilized. Examples of such computer-readable storagemediums include, but are not limited to, a hard disk, a CD-ROM, anoptical storage device, a magnetic storage device, a ROM (Read OnlyMemory), a PROM (Programmable Read Only Memory), an EPROM (ErasableProgrammable Read Only Memory), an EEPROM (Electrically ErasableProgrammable Read Only Memory) and a Flash memory. In the context ofthis document, a computer-usable or computer-readable medium may be anymedium that can contain, store, communicate, propagate, or transport theprogram for use by or in connection with the instruction executionsystem, apparatus, or device.

Further, it is expected that one of ordinary skill, notwithstandingpossibly significant effort and many design choices motivated by, forexample, available time, current technology, and economicconsiderations, when guided by the concepts and principles disclosedherein will be readily capable of generating such software instructionsand programs and integrated circuits (ICs) with minimal experimentation.For example, computer program code for carrying out operations ofvarious example embodiments may be written in an object orientedprogramming language such as Java, Smalltalk, C++, Python, or the like.However, the computer program code for carrying out operations ofvarious example embodiments may also be written in conventionalprocedural programming languages, such as the “C” programming languageor similar programming languages. The program code may execute entirelyon a computer, partly on the computer, as a stand-alone softwarepackage, partly on the computer and partly on a remote computer orserver or entirely on the remote computer or server. In the latterscenario, the remote computer or server may be connected to the computerthrough a local area network (LAN) or a wide area network (WAN), or theconnection may be made to an external computer (for example, through theInternet using an Internet Service Provider).

The Abstract of the Disclosure is provided to allow the reader toquickly ascertain the nature of the technical disclosure. It issubmitted with the understanding that it will not be used to interpretor limit the scope or meaning of the claims. In addition, in theforegoing Detailed Description, it can be seen that various features aregrouped together in various embodiments for the purpose of streamliningthe disclosure. This method of disclosure is not to be interpreted asreflecting an intention that the claimed embodiments require morefeatures than are expressly recited in each claim. Rather, as thefollowing claims reflect, inventive subject matter lies in less than allfeatures of a single disclosed embodiment. Thus the following claims arehereby incorporated into the Detailed Description, with each claimstanding on its own as a separately claimed subject matter.

What is claimed is:
 1. A communication system, comprising: a pluralityof radio voice communication devices for performing transmission andreception of first responder voice communications during a currentpublic safety incident; an unmanned aerial vehicle (UAV) having anintegrated communication device for monitoring the voice communications;and a processing unit for controlling the UAV and providing audio andvoice analytics to detect and learn voice commands and associate thelearned voice commands with UAV tasks assigned during the current publicsafety incident, the learned voice commands and associated UAV tasks forthe current public safety incident being processed for predictiveanalytics for automated control of the UAV at future public safetyincident using voice commands detected at the future incident.
 2. Thecommunication system of claim 1 wherein the predictive analyticsincluding weighting the UAV tasks and combining UAV tasks that can beperformed together and updating the weighting in response to futureincident UAV responses.
 3. The communication system of claim 1 whereinthe processing unit further performs a radio frequency (RF) survey viathe UAV communication device to identify personnel based on their uniqueradio ID and associate public safety (PS) commands with UAV tasksassociated with the identified personnel and incident type.
 4. Thecommunication system of claim 1 wherein the UAV streams video ofoperations from a plurality of areas of the incident in conjunction withdetected first responder voice commands.
 5. The communication system ofclaim 1 wherein the processing unit adds captions to UAV video that isnot synced with the first responder voice command to identify potentialanomalies.
 6. The communication system of claim 1 wherein the processingunit detects a voice command at a future incident and augments UAV tasksbased on the detected command.
 7. The communication system of claim 1wherein the processing unit detects a location by understanding aphysical location where a voice command originated and assessing asimilar voice command from a nearby location.
 8. The communicationsystem of claim 1 wherein the processing unit further filters outcommands that it cannot perform and substitutes a task that the UAV canperform related to the command.
 9. The communication system of claim 1,wherein UAV acquired data is transferred to first responders based onfirst responder role and incident parameter.
 10. A method forcontrolling a UAV at a public safety incident, comprising: deploying aUAV at an incident scene, the UAV performing pre-defined tasks based onincident type: building a dataset of UAV tasks based on detected voicecommands and incident type; performing predictive analytics on the tasksfor application at a future incident using the dataset; weighting andcombining tasks based on predetermined parameters; and automaticallyperforming predicted tasks at the future incident based on incidenttype.
 11. The method of claim 10, further comprising: detecting a voicecommand at a future incident; and automatically controlling UAV tasksbased on the detected command.
 12. The method of claim 10 furthercomprising: filtering out voice commands that the UAV cannot perform;and substituting a task that the UAV can perform related to the command.13. The method of claim 10 further comprising performing a radiofrequency (RF) survey via a UAV communication device to identifypersonnel based on their unique radio ID and associate public safety(PS) commands with UAV tasks associated with the identified personneland incident type.
 14. The method of claim 10 further comprisingstreaming video of operations from a plurality of areas of the incidentin conjunction with detected first responder voice commands.
 15. Themethod of claim 10 further comprising adding captions to UAV video thatis not synced with a first responder voice command to identify potentialanomalies.
 16. The method of claim 10 further comprising detecting avoice command at a future incident and augments UAV tasks based on thedetected command.
 17. The method of claim 10 further comprisingdetecting a location by understanding physical location where a voicecommand originated and assessing similar voice command from a nearbylocation.
 18. The method of claim 10 further comprising filtering outcommands cannot be performed by the UAV and substitutes a task that theUAV can perform related to the command.
 19. The method of claim 10further comprising transferring UAV data to first responders based onfirst responder role and incident parameter.
 20. The method of claim 10further comprising using a machine learning system to perform theweighting and combining.